Portable navigation devices (PNDs) that include GNSS (Global Navigation Satellite Systems) signal reception and processing functionality are well known and are widely employed as in-car or other vehicle navigation systems. Such devices include a GNSS antenna, such as a GPS antenna, by means of which satellite-broadcast signals, including location data, can be received and subsequently processed to determine a current location of the device. The PND device may also include electronic gyroscopes and accelerometers which produce signals that can be processed to determine the current angular and linear acceleration, and in turn, and in conjunction with location information derived from the GPS signal, velocity and relative displacement of the device and this vehicle in which it is typically mounted. Such sensors are most commonly provided in in-vehicle navigation systems, but may also be provided in the PND device itself.
In recent years, GPS has started to be used for pedestrian and outdoor applications. For example, sports watches that include GPS antennas have started to be used by joggers, runners, cyclists and other athletes and outdoor enthusiasts as a means to obtain real-time data of their speed, distance travelled, etc. The GPS data is also typically stored on such devices such that it can be analysed after the athlete has finished their activity, e.g. in some cases by transferring the collected data to a computer or website to be displayed on a digital map.
In conventional PNDs, vehicle speed and distance is often calculated using the measured ground speed of the vehicle obtained from the GNSS signals, and more specifically derived from the carrier phase tracking loops. For example, the distance travelled by the vehicle between two epochs (or specific instants in time when an updated GPS signal is received) can be calculated by integrating, either numerical or vector as appropriate, the vehicle's velocity vector over time. The well-known errors experienced with GPS signals, such as the multi-path effect, can also often be mitigated or at least reduced in vehicle navigation through various filtering techniques, such as Kalman filtering and map matching.
As will be easily appreciated, the dynamical behavior of pedestrians and other outdoor enthusiasts is very different from that of vehicles. For example, vehicles are limited in most circumstances to travel on a set road network, and thus will usually only experience limited and predictable changes in direction. In contrast, pedestrians, cyclists, etc. have no such restrictions (or are at least subject to significantly fewer restrictions) and thus have more complex dynamical movements. Furthermore, in dense urban environments, pedestrians will also often walk on pavements (or sidewalks), and thus will typically be closer to buildings than vehicles. This has the effect of reducing satellite visibility, thereby degrading horizontal dilution of precision (HDOP).
In view of these differences in dynamical behavior, there can be some difficulties in obtaining an accurate representation of a route followed by such a device, and hence by the user thereof. It would therefore be desirable to provide a method for providing a more accurate representation of a route travelled by a user of a device, particular one having location determining and tracking capability, such as a portable training device.